Main.Estimation History

Hide minor edits - Show changes to markup

May 25, 2013, at 12:48 AM by 69.169.188.188 -
Changed line 7 from:

MHE Tutorial in Simulink / MATLAB

to:

MHE with Simulink and MATLAB

May 25, 2013, at 12:47 AM by 69.169.188.188 -
Changed line 11 from:

Youtube video to be posted soon

to:

(:html:)<iframe width="560" height="315" src="http://www.youtube.com/embed/ZVUtVf8wOkg?rel=0" frameborder="0" allowfullscreen></iframe>(:htmlend:)

May 25, 2013, at 12:14 AM by 69.169.188.188 -
Added lines 9-10:
Changed line 13 from:

MHE mode in APM

to:

MHE in APMonitor

May 25, 2013, at 12:05 AM by 69.169.188.188 -
Changed line 7 from:

Tutorial on Implementing MHE in Simulink / MATLAB

to:

MHE Tutorial in Simulink / MATLAB

May 25, 2013, at 12:04 AM by 69.169.188.188 -
Deleted lines 3-4:

Moving Horizon Estimation

May 25, 2013, at 12:03 AM by 69.169.188.188 -
Added lines 1-4:

(:title Moving Horizon Estimation:) (:keywords nonlinear, model, predictive control, moving horizon, differential, algebraic, modeling language:) (:description Tutorial in Simulink / MATLAB for implementing Moving Horizon Estimation for linear or nonlinear systems.:)

Changed lines 7-8 from:

The DBS file parameter imode is used to control the simulation mode. This option is set to 5 for dynamic parameter estimation.

to:

Moving Horizon Estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches like the Kalman filter, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution.

Tutorial on Implementing MHE in Simulink / MATLAB

Youtube video to be posted soon

MHE mode in APM

The DBS file parameter imode is used to control the simulation mode. This option is set to 5 for dynamic parameter estimation or MHE.

Changed line 28 from:

Moving horizon estimation uses a moving window of previous model predictions and process measurements. As additional measurements arrive, the model is updated with the new information.

to:

Moving horizon estimation uses a moving window of previous model predictions and process measurements. As additional measurements arrive, the model is updated with the new information.

May 24, 2013, at 11:55 PM by 69.169.188.188 -
Deleted lines 16-17:
September 11, 2010, at 09:17 PM by 206.180.155.75 -
Changed lines 16-18 from:

Moving horizon estimation uses a moving window of previous model predictions and process measurements. As additional measurements arrive, the model is updated with the new information.

to:

Moving horizon estimation uses a moving window of previous model predictions and process measurements. As additional measurements arrive, the model is updated with the new information.

Attach:mhe.gif Δ

September 30, 2008, at 09:21 AM by 158.35.225.227 -
Changed lines 1-7 from:

APMonitor is commercially available software that brings estimation into an optimization framework. With the APMonitor Modeling Language, nonlinear dynamic models are rapidly prototyped and deployed. The APMonitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements in an approach termed Moving Horizon Estimation (MHE). MHE is desireable for problems with:

to:

Moving Horizon Estimation

The DBS file parameter imode is used to control the simulation mode. This option is set to 5 for dynamic parameter estimation.

NLC.imode = 5

Moving horizon estimation is optimization of model parameters based on a time series of data measurements. The APMonitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements. This approach is desireable for problems with:

Changed lines 9-14 from:

Constraints Nonlinear Models Infrequent Measurements Explicit Measurement Ranking Rejection of Statistically Insignificant Noise and Outliers Reliable real-time solutions

to:
  • Constraints
  • Nonlinear Models
  • Infrequent Measurements
  • Explicit Measurement Ranking
  • Rejection of Statistically Insignificant Noise and Outliers
  • Reliable real-time solutions
September 29, 2008, at 12:51 PM by 158.35.225.229 -
Added lines 1-10:

APMonitor is commercially available software that brings estimation into an optimization framework. With the APMonitor Modeling Language, nonlinear dynamic models are rapidly prototyped and deployed. The APMonitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements in an approach termed Moving Horizon Estimation (MHE). MHE is desireable for problems with:

Constraints Nonlinear Models Infrequent Measurements Explicit Measurement Ranking Rejection of Statistically Insignificant Noise and Outliers Reliable real-time solutions

Moving horizon estimation uses a moving window of previous model predictions and process measurements. As additional measurements arrive, the model is updated with the new information.